2 citations · 2 across the 3 of their papers we have counts for
3 papers
LeDiFlow: Learned Distribution-guided Flow Matching to Accelerate Image Generation
Pascal Zwick, Nils Friederich, Maximilian Beichter +3
Enhancing the efficiency of high-quality image generation using Diffusion Models (DMs) is a significant challenge due to the iterative nature of the process. Flow Matching (FM) is…
On autoregressive deep learning models for day-ahead wind power forecasting with irregular shutdowns due to redispatching
Stefan Meisenbacher, Silas Aaron Selzer, Mehdi Dado +6
Renewable energies and their operation are becoming increasingly vital for the stability of electrical power grids since conventional power plants are progressively being displaced…
Transformer Training Strategies for Forecasting Multiple Load Time Series
Matthias Hertel, Maximilian Beichter, Benedikt Heidrich +4
In the smart grid of the future, accurate load forecasts on the level of individual clients can help to balance supply and demand locally and to prevent grid outages. While the num…